Add files via upload

2026-03-09 00:11:57 +00:00
parent 4b7eb54ee8
commit 8974a38ff6
11 changed files with 18979 additions and 0 deletions
@@ -0,0 +1,97 @@
+import numpy as np
+import pandas as pd
+import math
+
+def generate_small_radar_csv(filename="small_test_radar_data.csv"):
+    """
+    Generate a smaller, faster-to-process radar CSV
+    """
+    # Reduced parameters for faster processing
+    num_long_chirps = 8    # Reduced from 16
+    num_short_chirps = 8   # Reduced from 16  
+    samples_per_chirp = 128 # Reduced from 512
+    fs_adc = 400e6
+    
+    targets = [
+        {'range': 3000, 'velocity': 25, 'snr': 40},
+        {'range': 5000, 'velocity': -15, 'snr': 35},
+    ]
+    
+    data = []
+    chirp_number = 0
+    timestamp_ns = 0
+    
+    # Generate Long Chirps
+    for chirp in range(num_long_chirps):
+        for sample in range(samples_per_chirp):
+            i_val = np.random.normal(0, 3)
+            q_val = np.random.normal(0, 3)
+            
+            # Add targets
+            for target in targets:
+                range_bin = int(target['range'] / 40)
+                doppler_phase = 2 * math.pi * target['velocity'] * chirp / 50
+                
+                if abs(sample - range_bin) < 5:
+                    amplitude = target['snr'] * (8000 / target['range'])
+                    phase = 2 * math.pi * sample / 30 + doppler_phase
+                    i_val += amplitude * math.cos(phase)
+                    q_val += amplitude * math.sin(phase)
+            
+            magnitude_squared = i_val**2 + q_val**2
+            
+            data.append({
+                'timestamp_ns': timestamp_ns,
+                'chirp_number': chirp_number,
+                'chirp_type': 'LONG',
+                'sample_index': sample,
+                'I_value': int(i_val),
+                'Q_value': int(q_val),
+                'magnitude_squared': int(magnitude_squared)
+            })
+            
+            timestamp_ns += int(1e9 / fs_adc)
+        
+        chirp_number += 1
+        timestamp_ns += 137000
+    
+    # Generate Short Chirps
+    for chirp in range(num_short_chirps):
+        for sample in range(samples_per_chirp):
+            i_val = np.random.normal(0, 3)
+            q_val = np.random.normal(0, 3)
+            
+            for target in targets:
+                range_bin = int(target['range'] / 60)
+                doppler_phase = 2 * math.pi * target['velocity'] * (chirp + 2) / 40
+                
+                if abs(sample - range_bin) < 4:
+                    amplitude = target['snr'] * 0.6 * (6000 / target['range'])
+                    phase = 2 * math.pi * sample / 25 + doppler_phase
+                    i_val += amplitude * math.cos(phase)
+                    q_val += amplitude * math.sin(phase)
+            
+            magnitude_squared = i_val**2 + q_val**2
+            
+            data.append({
+                'timestamp_ns': timestamp_ns,
+                'chirp_number': chirp_number,
+                'chirp_type': 'SHORT', 
+                'sample_index': sample,
+                'I_value': int(i_val),
+                'Q_value': int(q_val),
+                'magnitude_squared': int(magnitude_squared)
+            })
+            
+            timestamp_ns += int(1e9 / fs_adc)
+        
+        chirp_number += 1
+        timestamp_ns += 174500
+    
+    df = pd.DataFrame(data)
+    df.to_csv(filename, index=False)
+    print(f"Generated small CSV: {filename}")
+    print(f"Total samples: {len(df)}")
+    return df
+
+generate_small_radar_csv()